@misc{MichaudSchjeideSchenkeSeegeretal.2022,
  author    = {Michaud Schjeide, Brit-Maren and Schenke, Maren and Seeger, Bettina and P{\"u}schel, Gerhard Paul},
  title     = {Validation of a Novel Double Control Quantitative Copy Number PCR Method to Quantify Off-Target Transgene Integration after CRISPR-Induced DNA Modification},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-56175},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-561755},
  pages     = {1 -- 14},
  year      = {2022},
  abstract  = {In order to improve a recently established cell-based assay to assess the potency of botulinum neurotoxin, neuroblastoma-derived SiMa cells and induced pluripotent stem-cells (iPSC) were modified to incorporate the coding sequence of a reporter luciferase into a genetic safe harbor utilizing CRISPR/Cas9. A novel method, the double-control quantitative copy number PCR (dc-qcnPCR), was developed to detect off-target integrations of donor DNA. The donor DNA insertion success rate and targeted insertion success rate were analyzed in clones of each cell type. The dc-qcnPCR reliably quantified the copy number in both cell lines. The probability of incorrect donor DNA integration was significantly increased in SiMa cells in comparison to the iPSCs. This can possibly be explained by the lower bundled relative gene expression of a number of double-strand repair genes (BRCA1, DNA2, EXO1, MCPH1, MRE11, and RAD51) in SiMa clones than in iPSC clones. The dc-qcnPCR offers an efficient and cost-effective method to detect off-target CRISPR/Cas9-induced donor DNA integrations.},
  language  = {en}
}
@misc{SchenkeSchjeidePuescheletal.2020,
  author    = {Schenke, Maren and Schjeide, Brit-Maren and P{\"u}schel, Gerhard Paul and Seeger, Bettina},
  title     = {Analysis of motor neurons differentiated from human induced pluripotent stem cells for the use in cell-based Botulinum neurotoxin activity assays},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1083},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47207},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472071},
  pages     = {22},
  year      = {2020},
  abstract  = {Botulinum neurotoxins (BoNTs) are potent neurotoxins produced by bacteria, which inhibit neurotransmitter release, specifically in their physiological target known as motor neurons (MNs). For the potency assessment of BoNTs produced for treatment in traditional and aesthetic medicine, the mouse lethality assay is still used by the majority of manufacturers, which is ethically questionable in terms of the 3Rs principle. In this study, MNs were differentiated from human induced pluripotent stem cells based on three published protocols. The resulting cell populations were analyzed for their MN yield and their suitability for the potency assessment of BoNTs. MNs produce specific gangliosides and synaptic proteins, which are bound by BoNTs in order to be taken up by receptor-mediated endocytosis, which is followed by cleavage of specific soluble N-ethylmaleimide-sensitive-factor attachment receptor (SNARE) proteins required for neurotransmitter release. The presence of receptors and substrates for all BoNT serotypes was demonstrated in MNs generated in vitro. In particular, the MN differentiation protocol based on Du et al. yielded high numbers of MNs in a short amount of time with high expression of BoNT receptors and targets. The resulting cells are more sensitive to BoNT/A1 than the commonly used neuroblastoma cell line SiMa. MNs are, therefore, an ideal tool for being combined with already established detection methods.},
  language  = {en}
}